In [1]:
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_samples, silhouette_score
import matplotlib.pyplot as plt
import seaborn as sns
import warnings

warnings.filterwarnings("ignore")
plt.style.use('fivethirtyeight')
In [44]:
pip install yellowbrick

Defaulting to user installation because normal site-packages is not writeable
Collecting yellowbrick
  Downloading yellowbrick-1.5-py3-none-any.whl (282 kB)
     ------------------------------------ 282.6/282.6 kB 562.7 kB/s eta 0:00:00
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Requirement already satisfied: six>=1.5 in c:\programdata\anaconda3\lib\site-packages (from python-dateutil>=2.7->matplotlib!=3.0.0,>=2.0.2->yellowbrick) (1.16.0)
Installing collected packages: yellowbrick
Successfully installed yellowbrick-1.5
Note: you may need to restart the kernel to use updated packages.

[notice] A new release of pip is available: 23.1.2 -> 23.2.1
[notice] To update, run: python.exe -m pip install --upgrade pip
In [3]:
data=pd.read_csv("Mall_Customers.csv")
data
Out[3]:
CustomerID Gender Age Annual Income (k$) Spending Score (1-100)
0 1 Male 19 15 39
1 2 Male 21 15 81
2 3 Female 20 16 6
3 4 Female 23 16 77
4 5 Female 31 17 40
... ... ... ... ... ...
195 196 Female 35 120 79
196 197 Female 45 126 28
197 198 Male 32 126 74
198 199 Male 32 137 18
199 200 Male 30 137 83

200 rows × 5 columns

In [5]:
data.describe
Out[5]:
<bound method NDFrame.describe of      CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)
0             1    Male   19                  15                      39
1             2    Male   21                  15                      81
2             3  Female   20                  16                       6
3             4  Female   23                  16                      77
4             5  Female   31                  17                      40
..          ...     ...  ...                 ...                     ...
195         196  Female   35                 120                      79
196         197  Female   45                 126                      28
197         198    Male   32                 126                      74
198         199    Male   32                 137                      18
199         200    Male   30                 137                      83

[200 rows x 5 columns]>
In [6]:
data.shape
Out[6]:
(200, 5)
In [8]:
data.info
Out[8]:
<bound method DataFrame.info of      CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)
0             1    Male   19                  15                      39
1             2    Male   21                  15                      81
2             3  Female   20                  16                       6
3             4  Female   23                  16                      77
4             5  Female   31                  17                      40
..          ...     ...  ...                 ...                     ...
195         196  Female   35                 120                      79
196         197  Female   45                 126                      28
197         198    Male   32                 126                      74
198         199    Male   32                 137                      18
199         200    Male   30                 137                      83

[200 rows x 5 columns]>
In [9]:
data.isnull().sum()
Out[9]:
CustomerID                0
Gender                    0
Age                       0
Annual Income (k$)        0
Spending Score (1-100)    0
dtype: int64
In [12]:
data.columns
Out[12]:
Index(['CustomerID', 'Gender', 'Age', 'Annual Income (k$)',
       'Spending Score (1-100)'],
      dtype='object')
In [16]:
data.dtypes
Out[16]:
CustomerID                 int64
Gender                    object
Age                        int64
Annual Income (k$)         int64
Spending Score (1-100)     int64
dtype: object
In [ ]:
##Exploration Data Analysis
In [17]:
m_age = data[data['Gender']=='Male']['Age'] # subset with males age
f_age = data[data['Gender']=='Female']['Age'] # subset with females age
In [26]:
fig, ax = plt.subplots(nrows = 1, ncols = 2, figsize = (16,5))

plt.subplot(2,2,1)
palette_color = ['#BAD7E9', '#EB455F']
sizes = [m_age.count(), f_age.count()]
gender = ['Male', 'Female']
plt.pie(sizes, labels=gender, colors=palette_color, explode=(0, 0.05), autopct='%.0f%%')
plt.title('Gender Division')

plt.subplot(2,1,2)
ax = sns.countplot(x = 'Gender' , data = data, palette=palette_color)
for label in ax.containers:
    ax.bar_label(label)
plt.title('Gender Count')

plt.show()
In [24]:
age_bins = range(17,77,7)

# males histogram
fig2, (ax1, ax2) = plt.subplots(1, 2, figsize=(12,5), sharey=True)
sns.distplot(m_age, bins=age_bins, kde=False, color='#BAD7E9', ax=ax1, hist_kws=dict(edgecolor="k", linewidth=2))
ax1.set_xticks(age_bins)
ax1.set_ylim(top=25)
ax1.set_title('Males')
ax1.set_ylabel('Count')
ax1.text(45,23, "TOTAL count: {}".format(m_age.count()))
ax1.text(45,22, "Mean age: {:.1f}".format(m_age.mean()))

# females histogram
sns.distplot(f_age, bins=age_bins, kde=False, color='#EB455F', ax=ax2, hist_kws=dict(edgecolor="k", linewidth=2))
ax2.set_xticks(age_bins)
ax2.set_title('Females')
ax2.set_ylabel('Count')
ax2.text(45,23, "TOTAL count: {}".format(f_age.count()))
ax2.text(45,22, "Mean age: {:.1f}".format(f_age.mean()))

plt.show()
In [ ]:
##Ploting relations between Age , Annual Income and Spending Score
In [27]:
plt.figure(1 , figsize = (15 , 6))
n = 0

for x in ['Age' , 'Annual Income (k$)' , 'Spending Score (1-100)']:
    n += 1
    plt.subplot(1 , 3 , n)
    plt.subplots_adjust(hspace =0.5 , wspace = 0.5)
    sns.distplot(data[x] , bins = 20)
    plt.title('Distplot of {}'.format(x))

plt.show()
In [ ]:
##Plot Distribution using violin and Boxplot
In [28]:
plt.figure(1 , figsize = (14 , 7))
n = 0

for cols in ['Age' , 'Annual Income (k$)' , 'Spending Score (1-100)']:
    n += 1
    plt.subplot(1 , 3 , n)
    plt.subplots_adjust(hspace = 0.5 , wspace = 0.5)
    sns.boxplot(x = cols,y = 'Gender', data = data, palette=palette_color)
    plt.title('Boxplots' if n == 2 else '')
plt.show()
In [ ]:
##Plot Distribution column Age, Annual Income, and Spending Score relate each other.¶
In [29]:
plt.figure(1 , figsize = (15 , 10))
n = 0
for x in ['Age' , 'Annual Income (k$)' , 'Spending Score (1-100)']:
    for y in ['Age' , 'Annual Income (k$)' , 'Spending Score (1-100)']:
        n += 1
        plt.subplot(3 , 3 , n)
        plt.subplots_adjust(hspace = 0.5 , wspace = 0.5)
        sns.regplot(x = x , y = y , data = data)
        plt.ylabel(y.split()[0]+' '+y.split()[1] if len(y.split()) > 1 else y )
plt.show()
In [30]:
plt.figure(1 , figsize = (15 , 7))
n = 0

for cols in ['Age' , 'Annual Income (k$)' , 'Spending Score (1-100)']:
    n += 1
    plt.subplot(1 , 3 , n)
    plt.subplots_adjust(hspace = 0.5 , wspace = 0.5)
    sns.violinplot(x = cols , y = 'Gender' , data = data , palette = palette_color)
    sns.swarmplot(x = cols , y = 'Gender' , data = data)
    plt.ylabel('Gender' if n == 1 else '')
    plt.title('Boxplots & Swarmplots' if n == 2 else '')
plt.show()
In [31]:
plt.figure(1 , figsize = (15 , 6))

for gender in ['Male' , 'Female']:
    plt.scatter(x = 'Age' , y = 'Annual Income (k$)' , data = data[data['Gender'] == gender] ,
                s = 200 , alpha = 0.5 , label = gender)
plt.xlabel('Age'), plt.ylabel('Annual Income (k$)')
plt.title('Age vs Annual Income w.r.t Gender')
plt.legend()
plt.show()
In [32]:
plt.figure(1 , figsize = (15 , 6))

for gender in ['Male' , 'Female']:
    plt.scatter(x = 'Annual Income (k$)',y = 'Spending Score (1-100)' ,
                data = data[data['Gender'] == gender] ,s = 200 , alpha = 0.5 , label = gender)
plt.xlabel('Annual Income (k$)'), plt.ylabel('Spending Score (1-100)')
plt.title('Annual Income vs Spending Score w.r.t Gender')
plt.legend()
plt.show()
In [33]:
plt.figure(1 , figsize = (15 , 6))

for gender in ['Male' , 'Female']:
    plt.scatter(x = 'Age' , y = 'Annual Income (k$)' , data = data[data['Gender'] == gender] ,
                s = 200 , alpha = 0.5 , label = gender)
plt.xlabel('Age'), plt.ylabel('Annual Income (k$)')
plt.title('Age vs Annual Income w.r.t Gender')
plt.legend()
plt.show()
In [ ]:
## Correlation Matrix
In [34]:
fig, ax = plt.subplots(figsize=(14, 6))
data1 = data.copy()
data1['Gender'].replace(['Male', 'Female'], [0, 1], inplace=True)
color = sns.color_palette("ch:start=.2,rot=-.3", as_cmap=True)
sns.heatmap(data1.corr(), cmap=color, square=True, annot=True)

plt.show()
In [35]:
#Selecting columns for clusterisation with k-means
selected_cols = ["Spending Score (1-100)", "Annual Income (k$)", "Age"]
cluster_data = data.loc[:,selected_cols]
In [ ]:
## Data Scaling
In [36]:
scaler = StandardScaler()
cluster_scaled = scaler.fit_transform(cluster_data)
In [37]:
from sklearn.cluster import KMeans
wcss=[]
In [38]:
for i in range(1, 11):
    kmeans = KMeans(n_clusters= i, init='k-means++', random_state=0)
    kmeans.fit(cluster_data)
    wcss.append(kmeans.inertia_)
In [39]:
plt.plot(range(1,11), wcss)
plt.title('The Elbow Method')
plt.xlabel('no of clusters')
plt.ylabel('wcss')
plt.show()
In [45]:
from yellowbrick.cluster import KElbowVisualizer
!pip install yellobrick

model = KMeans(random_state=1)
visualizer = KElbowVisualizer(model, k=(2, 10))

visualizer.fit(cluster_data)
visualizer.show()
plt.show()

ERROR: Could not find a version that satisfies the requirement yellobrick (from versions: none)
ERROR: No matching distribution found for yellobrick

[notice] A new release of pip is available: 23.1.2 -> 23.2.1
[notice] To update, run: python.exe -m pip install --upgrade pip

Defaulting to user installation because normal site-packages is not writeable
In [46]:
KM_5_clusters = KMeans(n_clusters=5, init='k-means++').fit(cluster_data) # initialise and fit K-Means model

KM5_clustered = cluster_data.copy()
KM5_clustered.loc[:,'Cluster'] = KM_5_clusters.labels_
In [47]:
fig1, (axes) = plt.subplots(1,2,figsize=(12,5))

scat_1 = sns.scatterplot(x='Annual Income (k$)', y='Spending Score (1-100)', data=KM5_clustered, hue='Cluster',
                ax=axes[0], palette='Set1', legend='full')

sns.scatterplot(x='Age', y='Spending Score (1-100)', data=KM5_clustered, hue = 'Cluster',
                palette='Set1', ax=axes[1], legend='full')

axes[0].scatter(KM_5_clusters.cluster_centers_[:,1],KM_5_clusters.cluster_centers_[:,2], marker='s', s=40, c="blue")
axes[1].scatter(KM_5_clusters.cluster_centers_[:,0],KM_5_clusters.cluster_centers_[:,2], marker='s', s=40, c="blue")
plt.show()
In [48]:
KM_clust_sizes = KM5_clustered.groupby('Cluster').size().to_frame()
KM_clust_sizes.columns = ["KM_size"]
KM_clust_sizes
Out[48]:
KM_size
Cluster
0 39
1 79
2 23
3 37
4 22
In [49]:
import plotly as py
import plotly.graph_objs as go

def tracer(db, n, name):
    '''
    This function returns trace object for Plotly
    '''
    return go.Scatter3d(
        x = db[db['Cluster']==n]['Age'],
        y = db[db['Cluster']==n]['Spending Score (1-100)'],
        z = db[db['Cluster']==n]['Annual Income (k$)'],
        mode = 'markers',
        name = name,
        marker = dict(
            size = 5
        )
     )

trace0 = tracer(KM5_clustered, 0, 'Cluster 0')
trace1 = tracer(KM5_clustered, 1, 'Cluster 1')
trace2 = tracer(KM5_clustered, 2, 'Cluster 2')
trace3 = tracer(KM5_clustered, 3, 'Cluster 3')
trace4 = tracer(KM5_clustered, 4, 'Cluster 4')

data = [trace0, trace1, trace2, trace3, trace4]

layout = go.Layout(
    title = 'Clusters by K-Means',
    scene = dict(
            xaxis = dict(title = 'Age'),
            yaxis = dict(title = 'Spending Score'),
            zaxis = dict(title = 'Annual Income')
        )
)

fig = go.Figure(data=data, layout=layout)
py.offline.iplot(fig)